See how to:
Define MSA - An experiment designed to measure measurement system limitations to determine if measurement system is good enough to use or needs improvement
- Understand difference between Evaluting the Measurement Process (EMP) and Gage R&R
EMP provides a statistical approach for using measurement data effectively and creates gauge classificationsbased on probability of detecting a shift in the process
Classical Gauge R&R uses arbitrary thresholds for gauge classification (%R&R) but may lack information needed to use measurement data effectively
Use EMP to provide a statistical approach for using measurement data effectively for non-nondestructive case study
Build experimental design
Analyze the experiment data
Interpret Average and Standard Deviation chart results
Determine variance components estimates
- Interpret Paralleism Plots, Bias Comparison, EMP Reports , including interclass correlations
- Understand gauge deployment recommendations based on probability of detecting a shift in the process
Questions answered by Jerry Fish @JerryFish , Clark Ledbetter @Clark_Ledbetter and Jason Wiggins @jason at the lve webinar:
Q: What do you mean by 'chunky data'?
A: Think of a histogram that describes a normal distribution. A "Chunky" histogram might have only 5 or 6 bins describing the distribution. "Non-chunky" data might have 15 or 20 bars (bins) describing the distribution, so it is much smoother.
Q: Does it matter in the average chart that one point was within the ULC/LCL?
A: No, it just means that that particular part is close to the overall average of the parts. You just want most of the parts outside the UCL/LCL range.
Q: Is the EMP methoday different than variability charts?
A: Using the gauge variability chart, you're essentially kind of looking at the same setup. So you could use that platform and it's generating, the gauge variability output. That would be very similar to the variability chart with the the means connected just by showing the data and then the line goes through through the middle. The graphic is similar. What you get in this case are the control limits and n>the ability to kind of infer that a are my are my parts outside the control limit.
Q: What if you have more factors?
A: You can add more factors (like multiple gauges, multiple plants, etc.) and you can declare those as Crossed or Nested factors. That is handled in the setup (see DOE for measurement systems in JMP), and then analyzed similarly to what Jason just did.
Q: In the Med. Device Industry, the FDA has warned that rounding is not allowed and is considered falsification of data. Now the question- is it a problem if we don't round?
A: The way you conduct any analysis in a controlled industry is specific to that industry, so following their guidelies is critical. You must discern what you can do from what you should do,
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